ERN from EF bias
Hannah, 3/7/2017
Data from previously processed EEG files from CU and MU were used. Before I got them, the following was done:
- CNT file was merged with dat file to add response latency and accuracy information to CNT file
- CNT was re-referenced to average mastoids reference
- Blinks were corrected for
- A filter was applied (need to look up the settings for that)
- Files were response-locked, with an epoch of -400 to 500
I subsequently:
- Baseline corrected EEG files (using a baseline of -400 to -200)
- Performed an automatic artifact rejected procedure (trials with +- 75 uV were rejected, only using 9 electrodes of interest as the criteria)
Trials were included if:
1) The RT was between 200 and 500 ms
2) A response was made (i.e., no miss trials)
3) The trial wasn’t rejected in artifact rejection procedure
The following subjects were excluded:
- 1040 (doesn’t have full number of trials)
- 2023 (problems with EEG data)
- 2077 (problems with EEG data)
- 2089 (problems with EEG data)
- 2151 (problems with EEG data)
- 2157 (problems with EEG data)
- 2181 (problems with EEG data)
- 2187 (doesn’t have full number of trials)
Each subject did 384 experimental trials (prime-only trials were also included but not in the 384).
FZ, F3, F4, FCZ, FC3, FC4, C3, CZ, C4 (9 electrodes) were included.
Total sample is 134 subjects, 60 from CU and 74 from MU.
1. Examine correlation between subjects’ ERN mean amplitude and total number of errors
All conditions together:
## pearsons.r pvalue
## cor 0.08327981 0.3387364By condition separately:
## condition pearsons.r pvalue
## 1 Black-gun -0.028 0.745
## 2 Black-tool 0.244 0.004
## 3 White-gun -0.051 0.559
## 4 White-tool 0.104 0.230
## Saving 7 x 5 in image2. ERN/CRN grand averages
Negative is plotted upward.
To test the mean amplitude of the ERNs, a model was fitted with Race and Object as predictors. The intercept, slopes of Race and Object, and their interaction were allowed to vary by subject. The intercept was allowed to vary by Electrode nested within Subject.
Race and Object were both effect coded.
Random effects:
## Groups Name Std.Dev. Corr
## Electrode:Subject (Intercept) 1.63488
## Subject (Intercept) 2.81651
## Race.e 0.95942 -0.108
## Object.e 1.25933 -0.021 -0.002
## Race.e:Object.e 0.88610 -0.009 -0.198 -0.049
## Residual 7.56401Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.165 0.250 132.629 -4.664 0.000
## Race.e 0.638 0.088 128.279 7.248 0.000
## Object.e -0.518 0.113 129.698 -4.591 0.000
## Race.e:Object.e 0.218 0.082 124.091 2.652 0.0093. Looking at the ERN over the course of the experiment: Time-on-task
Slopes and estimates of lines are from the MLM, not fitted with OLS. Negative is plotted downward.
Simple slopes
Error order number is rescaled to range from 0 to 10.
## Estimate SE ci95_lower ci95_upper Race Object Color
## 1 -0.159 0.00944 -0.178 -0.1397 Black gun light blue
## 2 -0.115 0.00985 -0.135 -0.0958 Black tool dark blue
## 3 -0.162 0.00943 -0.180 -0.1426 White gun light red
## 4 -0.150 0.00970 -0.169 -0.1303 White tool dark redModel output
The intercept, slopes of current and previous trial condition and their interaction are allowed to vary by subject. Categorical variables are effect coded.
Trial is rescaled to range from 0 to 10.
Random effects:
## Groups Name Std.Dev. Corr
## Electrode:Subject (Intercept) 2.01462
## Subject (Intercept) 3.00823
## Race.e 0.56335 0.229
## Object.e 0.65431 -0.255 -0.019
## Race.e:Object.e 0.47315 -0.212 0.088 -0.045
## Residual 8.39359Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.3829 0.2679 135.3453 16.3595 0.0000
## Race.e 0.4937 0.0563 201.9198 8.7776 0.0000
## Object.e -0.7666 0.0632 184.3061 -12.1352 0.0000
## Race.e:Object.e 0.1245 0.0497 233.1638 2.5074 0.0128## Estimate Std. Error df t value Pr(>|t|)
## Trial.s -0.146 0.005 369247.4 -30.457 0.000
## Race.e:Trial.s -0.009 0.005 369304.4 -1.931 0.053
## Object.e:Trial.s 0.014 0.005 369269.4 2.860 0.004
## Race.e:Object.e:Trial.s -0.008 0.005 369303.5 -1.631 0.1034. Looking at the ERN over the course of the experiment: Ordered errors
- Each error is order for each subject
Slopes and estimates of lines are from the MLM, not fitted with OLS. Negative is plotted downward.
Simple slopes
Error order number is rescaled to range from 0 to 10.
## Estimate SE ci95_lower ci95_upper Race Object Color
## 1 0.0158 0.0330 -0.0502 0.0819 Black gun light blue
## 2 0.1383 0.0257 0.0869 0.1897 Black tool dark blue
## 3 0.0691 0.0282 0.0127 0.1254 White gun light red
## 4 0.0798 0.0294 0.0210 0.1386 White tool dark redModel output
The intercept, slopes of current and previous trial condition and their interaction are allowed to vary by subject. Categorical variables are effect coded.
For each subject, error trials are numbered (in order) and rescaled to range from 0 to 10.
Random effects:
## Groups Name Std.Dev. Corr
## Electrode:Subject (Intercept) 1.63498
## Subject (Intercept) 2.81125
## Race.e 0.95802 -0.103
## Object.e 1.25102 -0.029 0.000
## Race.e:Object.e 0.88415 -0.003 -0.201 -0.044
## Residual 7.56246Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.3642 0.2524 139.3330 -5.4048 0.0000
## Race.e 0.6392 0.0965 186.1406 6.6254 0.0000
## Object.e -0.6049 0.1189 163.7924 -5.0869 0.0000
## Race.e:Object.e 0.2899 0.0911 189.6321 3.1842 0.0017## Estimate Std. Error df t value Pr(>|t|)
## TrialOrder.s 0.076 0.015 85467.15 5.151 0.000
## Race.e:TrialOrder.s -0.001 0.015 61462.79 -0.090 0.929
## Object.e:TrialOrder.s 0.033 0.015 74506.34 2.278 0.023
## Race.e:Object.e:TrialOrder.s -0.028 0.015 56136.80 -1.922 0.0555. Looking at the ERN over the course of the experiment: Ordered errors, centered by subject
- Each error is order for each subject, then centered for each subject
Slopes and estimates of lines are from the MLM, not fitted with OLS. Negative is plotted downward.
Simple slopes
Error order number is rescaled to range from -5 to 5.
## Estimate SE ci95_lower ci95_upper Race Object Color
## 1 0.0187 0.0332 -0.0476 0.085 Black gun light blue
## 2 0.1305 0.0257 0.0791 0.182 Black tool dark blue
## 3 0.0733 0.0282 0.0170 0.130 White gun light red
## 4 0.0782 0.0295 0.0192 0.137 White tool dark redModel output
The intercept, slopes of current and previous trial condition and their interaction are allowed to vary by subject. Categorical variables are effect coded.
For each subject, error trials are numbered (in order), centered, and rescaled to range from -10 to 10.
Random effects:
## Groups Name Std.Dev. Corr
## Electrode:Subject (Intercept) 1.63498
## Subject (Intercept) 2.81689
## Race.e 0.95783 -0.105
## Object.e 1.25814 -0.021 -0.003
## Race.e:Object.e 0.88589 -0.007 -0.197 -0.053
## Residual 7.56245Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.1659 0.2498 132.6195 -4.6674 0.0000
## Race.e 0.6359 0.0880 128.3133 7.2294 0.0000
## Object.e -0.5168 0.1128 129.6916 -4.5828 0.0000
## Race.e:Object.e 0.2155 0.0821 124.1788 2.6240 0.0098## Estimate Std. Error df t value Pr(>|t|)
## TrialOrder.c.s 0.075 0.015 85958.35 5.139 0.000
## Race.e:TrialOrder.c.s 0.001 0.015 85901.67 0.041 0.968
## Object.e:TrialOrder.c.s 0.029 0.015 85918.21 1.994 0.046
## Race.e:Object.e:TrialOrder.c.s -0.027 0.015 85887.81 -1.827 0.068